Accurate and consistent depth estimation for light field camera arrays

In this paper, we propose a depth estimation framework for light field camera arrays. The goal of the proposed framework is to compute consistent depth information over the multiple cameras which is hardly achieved by conventional approaches based on the pairwise stereo matching. We first perform stereo matchings on adjacent image pairs using a convolutional neural network-based correspondence scoring model. Once the local disparity maps are estimated, we consolidate the disparity values to make them globally sharable over the multiple views. We finally refine the depth values in the image domain by introducing a novel image segmentation method considering edges in the image to obtain a semantic-aware global depth map. The proposed framework is evaluated on three different real world scenarios, and the experimental results validate that our proposed method produces accurate and consistent depth maps for images captured by the light field camera arrays.